Hedging efficiency is the degree to which hedging in a futures market compensates for spot market price risks. Perhaps, the best hedging efficiency indicator is computed as
Ft - Fo
X 100 (8.1) Rt - Ro - C
Where Ft is the futures price at time t; Fo is the futures price at time O, Rt is the spot price at time t; Ro is the spot price at time O; and C is the carrying cost for period t – o. Where, however, such calculation yields a percentage in excess of 100, the formula used is:
Ft - Fo
2 - X 100% (8.2) Rt - Ro - C
This is because hedge which ‘over-compensates’ one category of hedger, by the same token ‘under-compensates’ the other category of hedger. Thus, the
hedging efficiency indicators can be aggregated and used for further analysis in two ways: by means of frequency distribution and by means of computing aggregative averages. By means of frequency distribution involves the classification of hedges into effective and ineffective hedges followed by further classification based on degree of effectiveness or ineffectiveness.
Here, the frequencies in each category and sub-category can then be used to assess market performance. On the other hand, by means of computing aggregative averages involves the calculation of average rates of hedging efficiency for individual months, years, etc., by aggregating the results of individual hedges. Thus a good average to use is the weighted arithmetic means, with the extent of spot market price risks as the weights.
The portfolio analysis as an empirical technique attempts to ascertain the optimal level of hedging to be adopted in order to optionise the risk-return trade-off. This involves the construction of a portfolio selection problem in the following form:
ERp = QuE (Rt – Ro) + Qb – E (Ft – Fo) (8.3)
Where ERp is the expected return on hedged portfolio; Qu is the unhedged stock; Qb is the hedged stock; E (Rt – R o) is the expected change in the ready (spot) price and (ft – fo) is the expected change in the futures price. In
= Qh/Qu) such that the portfolio risk is minimized. This is given by the following formula:
h* = cov (R, F)/Var(F) (8.4)
where cov(R,F) is covariance between changes in R and F; while Var (F) is the variance of futures price changes. However, other test introduce a risk aversion function into the analysis and attempt to select an optimum hedge ratio which results in the best risk return trade-off. Here, many tests involve regression of futures against spot prices to ascertain whether or not futures are good predictors of spot prices or to test other typo theses on the interrelationships between them. The extent of correlation and its significance are used to draw conclusions on the efficiency of futures trading. Similarly, a number of texts have been detected at studying whether or not speculators earn a positive return; whether they earn returns due to normal backwardation or due to superior forecasting ability; whether prices follow a random walk, and so on. Variate difference analysis is another method used to study the effect of futures trading on price fluctuations. Here, price changes are divided into two parts: a systematic component which reflects economic fundamentals and an error term which reflects derivations from economic fundamentals. Therefore these effects of futures trading on the error portion of the fluctuations can be studied.
The following model shows the relationship between foreign participation in futures market and invisible earnings of foreign exchange. Assume
that V = t (8.5) where t is a constant, measuring the relationship P
between trading volume and average open position, can be called the turnover ratio. This ratio depends on the frequency with which market operator’s close out old contracts and opens new contracts. This frequency generally depends on the investment attitudes of market participants which do not change significantly in the short run. Thus, the foreign exchange earned on margin moves is
F1 = Cm (8.6)
But M = Mp and therefore F1 = CMP but from equation (8.5), P = V
t (8.6a) Thus,
F1 = cmv
t (8.7) The foreign exchange earned through brokerage is
F2 = bv (8.8)
The foreign exchange earned through sundry charges levied by the association
F3 = Av (8.9)
F4 = dv (8.10)
The foreign exchange earned through remittance and other charges levied by banks. foreign participants. In the above model, V is the trading volume per annum by foreign participants (in money terms); P is the average net open position held by foreign participants ( in money terms); M is the average quantum of margin money of foreign participants; F is the foreign exchange earned per annum; C is the marginal interest rate on foreign currency (borrowing cost) b is the brokerage rate, a is the rate of laga and other charge levied by the association; d is the rate of stamp duty levied by the government; r is the average rate o remittance costs and other banking charges per transaction and m is the rate of margin
9.0 CONCLUSION
Indeed, fresh and new instruments and trading techniques have occurred within the African economy and the global financial system in recent times.
The pace at which this innovation in financial services is accelerating under the combined pressure of increased competition, rising cost and growing risk is alarming. These forces are profoundly and continuously re-shaping the structure and operations of the entire financial system today. Africa is not left out of this global phenomenon as it is witnessing its own transformation within the financial landscape (though at a slow pace). A derivative instrument value from the value of some other financial instrument or variable. Here, a stock option is a derivative because it derives value fro m the value of a stock while assets based securities are derivatives because it derives its value from an underlying asset.
There should be no doubt that derivatives are an absolutely essential tool in a modern company’s survival kit. They are basically necessary because risk exist. However, some of the risks are man made and can be controlled at a macro level by means other than futures and options. A growing rend is fierce competition between different futures exchanges. The benefit of this is improved trading convenience and lower cost. But exchanges are in
to maximize turnover lead to a clear conflict of interest which could lead to a weakening f the whole chain. Perhaps, this may lead eventually to a reconsideration of the profit-oriented status of the exchanges in favor of non-profit organizations.
Unlike the developed economics, Africa does not have to be one hundred percent ready in order to start derivative market. What are actually important are the appropriate regulatory framework and the commitment to do it right from the onset. A well regulated settlement and clearing system, as well as strict standards for qualification to trade in derivatives instruments are equally needed. The nature of derivatives trading creates more opportunities for both insider and outsider abuse, and which is why the appropriate market surveillance procedures will need to be put in place. In other words, investor knowledge, market confidence; licensing requirements;
trading and clearing rules; efficient trading system; and liquidity of the underlying instruments are necessary prerequisites for the establishment of successful derivatives markets in Africa. Thus, the development of modern trading skills, like that of any other know-how, requires an action plan to build the needed institutions and train the concerned staff. However, before preparing an action plan, the staff, planners and policy makers involved in
derivative marketing need to acquire a fundamental understanding of the workings and the issues involved in the new era of derivative trading.
REFERENCES
Baer, J.B. and O.G. Saxon (1948) Commodity Exchanges and Futures Trading, New York: Harper Bros
Baesel, J. AND d. Grant (1982) “Equilibrium in the Futures Market,
Southern Economic Journal, vol. 49 (1982 pp 320 – 29 Black, F. and M. J. Scholes (1973) “The pricing of options and corporate
liabilities”, Journal of Political Economy, May
Brennan, M. J. (1985) “The supply of Storage”, American Economic Review, V.L. 48, pp 50 – 72.
CMD (1994) Emerging Futures and Options Markets, Washington:
International Finance Corporation
Friedman, M. (1953) Essays in Positive Economics,Chicago:
Chicago University press
Guss, B. A. and B. S. Yaney (1978) The Economics of futures Trading:
Reading, Selected, Edited and Introduced, London: Macmillan.
Hicks, J. R. (1964) Value and Capital, Oxford: Oxford University Press Houthakker, H. S. (1957) “Can speculators forecast prices”? Reviews of
Economics and statistics Vol. 39, pp 143 – 157
Houthakker, H. S. (1982) “The Extension of Futures. Trading to the Financial Sector”, Journal of Banking and Finance,
Vol. 6, pp 37 – 47.
Irwin, H. S. (1960) “The Nature of Risk. Assumption in the Trading on Organized Exchanges, American Economic Review, vol. 27, pp 267.
275
Johnson, L. L. (1960) “The Theory of Hedging and Speculation in Commodity Futures”,
Keynes, J. M. (1930) A Treatise on Money: Vol. II, London: Macmillan Knight, F. H. (1921) Risk, Uncertainty and Profit, Boston: Houghton
Mifflin
Naik, A. S. (1970) The effects of Futures Trading on prices, Bombay:
Somaiya Publications
Newberry, D. M. G. and J. E. Stiglitz (1981) The Theory of Commodity Price Stabilization, Oxford: Clarendon Press.
Nwaobi, G. C. (2006) Modern Econometric Modeling for Developing Economies II, Aba: Quanterb/ Jothol Publishing Press.
Pavaskar, M. G. (1976) The Economics of hedging, Bombay; Asia Publishing house.
Powers, M. J. (1970) “Does Futures Trading Reduce Price Fluctuations in the Cash Market”, American Economic Review, 460 – 464.
Preston, M.H. and B. S. Yaney (1960) “Inter-temporal Prince Relationships With Forward Markets: A Method of Analysis”, Economica vol. 37.
Razari, H. (1989) “The New Era of Petroleum Trading, World Bank Technical paper No. 96.
Samanathan, T. V. (1998) Derivatives, New Delhi: McGraw-Hill
Samuelson, P. A. (1976) “Is Real-World price a Tale Told by the Idiot of Chance”?
Stein, J. L. (1961) “The Simultaneous Determination of spot and Futures
Stein, J. l. (1986) The Economics of Futures Markets, Oxford: Basil Blackwell.
Stevenson, R. A. and R. M. Bear (1970) “Commodity Futures: Trends or Random Walks,” Journal of Finance, March, pp. 65 - 81
Stein, J.L. (1986) The Economics of Futures Markets, Oxford: Basil Blackwell.
Stevenson, R.A. and R.M. Bear (1970) “Commodity Futures: Trends or Random Walks,” Journal of Finance, March, pp. 65 – 81.
Still, H.R. (1969) “The Relationship between put and Call option prices”, Journal of Finance, December.
Tomek, W.G. and R.W. Gray (1970) “Temporal Relationships among Prices on Commodity Futures markets: Their Allocative and
Stabilizing Roles”, American Journal of Agricultural Economics, Vol.
52.
Williams, J. (1994) The Economic Function of Futures Markets, Cambridge: Cambridge University press.
Working, H. (1949) The Theory of the Price of Storage,” American Economic Review, Vol. 31
Working, H. (1953) “Futures Trading and Hedging”, American Economic Review, pp. 314 – 343
Wright, B. D. and J. C. Williams (1982) “The Economic Role of Commodity Storage”, Economic Journal, September.